An Adaptive Multi-region Fusion Network for Dense Face Detection

In recent years, face detection has been widely applied in various intelligent monitoring systems. However, missed detections and low detection accuracy present challenges, such as small, blurred, and occluded faces in multi-face detection scenarios. To address these challenges, an adaptive multi-region fusion network is designed for dense face detection. First, in the shallow layers of the network, a multi-scale cross-stage fusion (MC4f) module is designed to replace the C3 module, which solves the issue of gradient explosion or disappearance in deep networks and promotes the effective convergence of the network on small datasets. An adaptive fusion explicit spatial vision centre (AESVC) is then designed between the backbone and neck networks to adaptively fuse local and global features to refine face information and enhance feature representation capabilities in complex tasks. Subsequently, a multi-scale parallel attention mechanism (MSPAM) is proposed to enhance the cross-scale fusion of facial features and reduce the loss of shallow features. Finally, to achieve accurate facial key point localisation and alignment, wing loss and A-loss functions are integrated, which balances the relationship between easy and difficult samples. Compared with the original model, the proposed model increases the mean average precision (mAP) by 1.75, 2.01, and 3.06% for easy, medium, and hard samples, respectively. The experimental results prove the effectiveness of the adaptive multi-region fusion network for dense face detection.